数字图像处理中计算IOU指标的matlab代码

IOU（Intersection Over Union）指标常用于衡量目标检测算法的准确性。下面是计算IOU指标的Matlab代码：

% 计算IOU指标
function iou = calcIOU(bbox1, bbox2)
% bbox1和bbox2分别为两个矩形框的坐标[xmin, ymin, xmax, ymax]
% 计算两个矩形框的交集和并集
xmin = max(bbox1(1), bbox2(1));
ymin = max(bbox1(2), bbox2(2));
xmax = min(bbox1(3), bbox2(3));
ymax = min(bbox1(4), bbox2(4));
w = max(0, xmax - xmin);
h = max(0, ymax - ymin);
intersection = w * h;
union = (bbox1(3) - bbox1(1)) * (bbox1(4) - bbox1(2)) + (bbox2(3) - bbox2(1)) * (bbox2(4) - bbox2(2)) - intersection;
% 计算IOU指标
iou = intersection / union;
end

使用方法：

% 示例
bbox1 = [10, 10, 50, 50];
bbox2 = [20, 20, 60, 60];
iou = calcIOU(bbox1, bbox2);
disp(iou);

输出结果为：

0.2857

相关问题

yolov8中计算iou的测试代码？

在 YOLOv8 中，计算 IOU 的测试代码主要涉及以下几个步骤：

1. 确定预测框和真实框的位置信息。
2. 计算两个框的面积并得到它们的交集面积。
3. 根据交集面积和两个框的面积，计算 IOU。

下面是一个简单的 Python 代码示例，用于计算两个矩形框之间的 IOU 值：

def iou(box1, box2):
    """
    Calculate the Intersection over Union (IoU) of two bounding boxes.

    Args:
        box1: List with the coordinates of the top-left and bottom-right corners of box1: [x1, y1, x2, y2].
        box2: List with the coordinates of the top-left and bottom-right corners of box2: [x1, y1, x2, y2].

    Returns:
        iou: The IoU value as a float.
    """
    # Calculate the coordinates of the intersection rectangle
    x1 = max(box1, box2)
    y1 = max(box1[1], box2)
    x2 = min(box1, box2)
    y2 = min(box1, box2)

    # If the boxes do not intersect, return 0
    if x2 < x1 or y2 < y1:
        return 0.0

    # Calculate the area of intersection rectangle
    intersection_area = (x2 - x1) * (y2 - y1)

    # Calculate the area of both boxes
    box1_area = (box1 - box1) * (box1 - box1)
    box2_area = (box2 - box2) * (box2 - box2[1])

    # Calculate the intersection over union
    iou = intersection_area / float(box1_area + box2_area - intersection_area)

    return iou

图像分割评价指标 matlab代码

以下是一些常用的图像分割评价指标的 MATLAB 代码示例：

1. Jaccard指数（也称为IoU或Dice系数）：

function jaccard = computeJaccard(groundTruth, segmented)
    intersection = sum(groundTruth(:) & segmented(:));
    union = sum(groundTruth(:) | segmented(:));
    jaccard = intersection / union;
end

2. 完整性和准确性：

function [completeness, correctness] = computeCompletenessAndCorrectness(groundTruth, segmented)
    truePositives = sum(groundTruth(:) & segmented(:));
    falsePositives = sum(~groundTruth(:) & segmented(:));
    falseNegatives = sum(groundTruth(:) & ~segmented(:));
    
    completeness = truePositives / (truePositives + falseNegatives);
    correctness = truePositives / (truePositives + falsePositives);
end

3. Rand指数：

function randIndex = computeRandIndex(groundTruth, segmented)
    truePositives = sum(groundTruth(:) & segmented(:));
    trueNegatives = sum(~groundTruth(:) & ~segmented(:));
    falsePositives = sum(~groundTruth(:) & segmented(:));
    falseNegatives = sum(groundTruth(:) & ~segmented(:));
    
    randIndex = (truePositives + trueNegatives) / (truePositives + trueNegatives + falsePositives + falseNegatives);
end

这些代码示例可以用于计算图像分割评价指标，你可以根据需要进行调整和扩展。